{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4051.0, 3200.0], [4064.0, 3206.0], [4077.0, 3205.0], [4081.0, 3192.0], [4080.0, 3181.0], [4068.0, 3174.0], [4068.0, 3166.0], [4090.0, 3179.0], [4092.0, 3193.0], [4085.0, 3209.0], [4063.0, 3214.0], [4043.0, 3210.0], [4034.0, 3197.0], [4033.0, 3187.0], [4044.0, 3171.0], [4039.0, 3183.0], [4047.0, 3190.0], [4044.0, 3170.0], [4049.0, 3161.0], [4050.0, 3172.0]]\n",
      "Point ( 4062.02342733, 3193.36500362 ) [[4051.0, 3200.0], [4064.0, 3206.0], [4077.0, 3205.0], [4081.0, 3192.0], [4080.0, 3181.0], [4068.0, 3174.0], [4068.0, 3166.0], [4090.0, 3179.0], [4092.0, 3193.0], [4085.0, 3209.0], [4063.0, 3214.0], [4043.0, 3210.0], [4034.0, 3197.0], [4033.0, 3187.0], [4044.0, 3171.0], [4039.0, 3183.0], [4047.0, 3190.0], [4044.0, 3170.0], [4049.0, 3161.0], [4050.0, 3172.0]]\n",
      "Point ( 4077.0, 3205.0 ) Point ( 4081.0, 3192.0 ) Point ( 4062.02342733, 3193.36500362 )\n",
      "Point ( 4092.0, 3193.0 ) Point ( 4085.0, 3209.0 ) Point ( 4062.02342733, 3193.36500362 )\n",
      "False\n"
     ]
    },
    {
     "data": {
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HSeedJ917r1vjBwAIG6EPFbd/v9ueYc4cadQo39UUF6V7Z7HPDb17J504kQbG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\nlsaPl848U5oyRXr5ZamqqtSjAgAAuSD0AXmQ2rC9FJjpQ75t3y7NmSM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6654a1910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapeanalyzer.centers import Center\n",
    "from shapeanalyzer.shapes import *\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.axis(\"equal\")\n",
    "fig_size = [15,15]\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "fp = open(\"data/water1.json\")\n",
    "data = json.loads(fp.read())\n",
    "fp.read()\n",
    "def plotcell(c):\n",
    "    x,y,h = c[\"x\"], c[\"y\"], c[\"h\"]\n",
    "    xs = [x-h,x-h,x+h,x+h,x-h]\n",
    "    ys = [y-h,y+h,y+h,y-h,y-h]\n",
    "    plt.plot(xs,ys,'g-')\n",
    "polys = []\n",
    "for d in data:\n",
    "    pts = []\n",
    "    for p in d:\n",
    "        pts.append(Point(p[0],p[1]))\n",
    "    pts.pop()\n",
    "    pts.pop()\n",
    "    polys.append(Polygon(pts))\n",
    "\n",
    "i = 6\n",
    "cent = Center(polys[i])\n",
    "cd = cent.Centroid()\n",
    "vc,pts,cells = cent.VisualCenter()\n",
    "pts = np.asarray(pts)\n",
    "sdat = np.asarray(data[i])\n",
    "print polys[i].coords()\n",
    "print cd, polys[i].isinside(cd)\n",
    "plt.plot(sdat[:,0], sdat[:,1],\"-\")\n",
    "plt.plot(pts[:,0],pts[:,1],\"g+\")\n",
    "for c in cells:\n",
    "    plotcell(c)\n",
    "plt.plot(cd.x, cd.y,\"om\")\n",
    "plt.plot(vc.x, vc.y,\"or\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
